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在 OpenClaw 中安装
/install mouse-yolo-factory
功能描述
Generate simulated scratch defects, run YOLO model inference with auto-labeling, and merge mouse product defect image datasets with version control.
安全使用建议
What to check before installing or running:
- The code implements the stated features, but the SKILL.md commands and some internal paths are hard-coded to D:/... — make sure you understand and relocate those paths to directories you control before running.
- The scripts expect heavy Python dependencies (torch, ultralytics, torchvision, opencv, pandas, numpy). The skill metadata does not declare these — install them in a controlled environment (preferably a virtualenv or container).
- The inference script writes detection logs to D:/aiagent/rag_database/detection_logs.jsonl and writes output images/labels into dataset folders. Inspect the logs if they may contain sensitive filenames or metadata.
- Run the code first in an isolated environment (container or VM) and review/modify the hard-coded paths and any file-write locations. Search the code for any other absolute paths before trusting it with production data.
- If you need tighter guarantees, ask the publisher to: remove hard-coded paths, declare required dependencies, and document exactly what files will be created/modified and where.
- Confidence is medium: nothing in the code indicates network exfiltration or obfuscated/malicious behavior, but the path assumptions and missing dependency declarations are implementation issues that could lead to accidental data exposure or file overwrites.
功能分析
Type: OpenClaw Skill
Name: mouse-yolo-factory
Version: 1.0.0
The skill bundle is a specialized toolset for YOLO-based defect detection on computer mice, including synthetic scratch generation, automated labeling, and dataset management. The code utilizes standard libraries such as OpenCV, NumPy, and Ultralytics for image processing and model inference. While it contains hardcoded file paths (e.g., D:/aiagent/...) and performs file deletions (shutil.rmtree) as part of its dataset merging logic, these actions are consistent with the stated purpose and show no signs of malicious intent, data exfiltration, or unauthorized remote access.
能力评估
Purpose & Capability
The code files implement scratch generation, YOLO inference (auto-labeling), and dataset merging, which aligns with the skill description. However the SKILL.md uses absolute Windows paths (D:/aiagent/aiagent_for_Mouse_Python_code/...) and the code itself creates a hard-coded RAG DB directory at D:/aiagent/rag_database — these hard-coded paths are not declared in metadata and may not match where the skill will be run.
Instruction Scope
SKILL.md instructs running scripts via absolute paths on D:, implying the agent or user should store/execute code there; the runtime code writes detection logs (JSONL) to a hard-coded local RAG path and writes/creates dataset folders and labels. While this is expected for dataset tooling, the instructions give no warning about these file writes and assume a Windows D: layout — this is scope creep relative to a simple 'run model' description and could overwrite or create files in unexpected locations.
Install Mechanism
There is no install spec (instruction-only + code files bundled). However the Python code depends on heavy native libraries (ultralytics, torch, torchvision, cv2/opencv, numpy, pandas) which are not declared. Users may attempt to run the scripts without these dependencies; installing them can be non-trivial and may require compiling native code. Absence of dependency declarations is an operational risk but not necessarily malicious.
Credentials
The skill requests no environment variables or credentials (which is good), but it writes logs to and creates directories under a hard-coded path (D:/aiagent/rag_database) and uses file-system locations for datasets. The skill does not declare these required config paths in metadata. There's no network exfiltration code, but local log files may include detection summaries — review these if they may contain sensitive image identifiers.
Persistence & Privilege
The skill does not request 'always: true' and does not modify other skills or global agent configuration. Its persistence is limited to creating directories and writing files (datasets, labels, and a local RAG JSONL log) within the file system; this is expected for dataset tooling.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install mouse-yolo-factory - 安装完成后，直接呼叫该 Skill 的名称或使用
/mouse-yolo-factory触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release of mouse-yolo-factory:
- Integrates YOLO-based defect detection for mouse products.
- Supports scratch defect image generation, model-based auto-labeling, and dataset merging/versioning.
- Provides clear command-line usage examples for each workflow.
- Includes detailed guidance on intended and excluded use cases.
元数据
常见问题
Mouse YOLO Factory 是什么？
Generate simulated scratch defects, run YOLO model inference with auto-labeling, and merge mouse product defect image datasets with version control. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 230 次。
如何安装 Mouse YOLO Factory？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install mouse-yolo-factory」即可一键安装，无需额外配置。
Mouse YOLO Factory 是免费的吗？
是的，Mouse YOLO Factory 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Mouse YOLO Factory 支持哪些平台？
Mouse YOLO Factory 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Mouse YOLO Factory？
由 Dwysbd（@dwysbd）开发并维护，当前版本 v1.0.0。
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